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A.I. Commerce Studio

Cloud and platform

Internal Kubernetes platform

A managed cluster with team namespaces, GitOps, policies and observability as a paved road for services.

Fictional scenario. An example situation that illustrates the approach. It is not a client project and describes no measured results.

Business problem

A fictional company runs about ten services on virtual machines with different delivery processes. New teams start slowly.

Assumptions

  • A managed service is used (AKS, EKS or GKE).
  • Services are already containers or can be.
  • There is a platform team.

Constraints

  • No direct changes in the cluster.
  • Each team sees only its namespaces.
  • Secrets are not in Git.

Reference architecture

Components by stage — an illustration, not a running system and not a certified design.

  1. 01Input

    • Git repositories
    • Image registry
  2. 02Delivery

    • GitOps controller
    • Admission policies
  3. 03Platform

    • Team namespaces
    • Ingress
    • Autoscaling
  4. 04Monitoring

    • Metrics
    • Logs
    • Traces
The diagram as a list
StageComponents
InputGit repositories, Image registry
DeliveryGitOps controller, Admission policies
PlatformTeam namespaces, Ingress, Autoscaling
MonitoringMetrics, Logs, Traces

Interfaces

FromToVia
CIRegistrySigned images
GitOpsClusterPull model
ServicesSecretsExternal secrets

Data flow

  1. Code is built and the image is signed.
  2. A Git change describes the desired state.
  3. GitOps applies the change.
  4. Policies reject unsuitable manifests.
  5. Monitoring shows health.

Security boundaries

  • RBAC per namespace.
  • Policies: no root, no privileged containers.
  • Secrets from an external store.

Where a person approves

  • Production manifest changes through review.
  • Policy exceptions.

Delivery phases

  1. 01 · Design

    Clusters, network, multi-tenancy.

  2. 02 · Foundation

    Cluster, GitOps, policies.

  3. 03 · Migration

    The first services.

  4. 04 · Operation

    Upgrades and capacity.

Limitations

  • Not every service needs Kubernetes.
  • It needs a platform team.

Trade-offs

  • Shared vs separate clusters — cost vs isolation.
  • Strict policies — slower team onboarding.

Facing something similar?

We start with an assessment of where you are and propose a realistic first phase.